Gut inference: A computational modelling approach

Smith, Ryan; Mayeli, Ahmad; Taylor, Samuel; Naegele, Jessyca; Khalsa, Sahib S.

doi:10.1016/j.biopsycho.2021.108152

Cited by 48 publications

(43 citation statements)

References 114 publications

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“…This leads decision-making to favor actions that optimize a trade-off between maximizing reward and information gain. The resulting patterns of perception and behavior predicted by active inference match well with those observed empirically (e.g., see Smith et al, 2021d , 2021c , 2020b ; Smith, Kuplicki, Teed, Upshaw, & Khalsa, 2020c ; Smith et al, 2021e , 2020e ). The neural process theory associated with active inference has also successfully reproduced empirically observed neural responses in multiple research paradigms and generated novel, testable predictions ( Friston, FitzGerald, Rigoli, Schwartenbeck, & Pezzulo, 2017a ; Schwartenbeck, FitzGerald, Mathys, Dolan, & Friston, 2015 ; Whyte & Smith, 2020 ).…”

Section: Introductionsupporting

confidence: 79%

“…We encourage the reader to do so to get a sense of the unique influences of different parameters on task behavior. For examples of papers that model learning using active inference, see Friston et al (2016a , 2017b ), Schwartenbeck et al (2019) , Smith et al (2021e) , Smith, Parr, and Friston (2019b) , Smith, Schwartenbeck, Parr, and Friston (2020d) , Smith et al (2020e) and Tschantz, Seth, and Buckley (2020) .…”

Section: Modeling Learningmentioning

confidence: 99%

“…8 ). To be clear, some neurophysiological predictions in active inference are not specific to a single neural implementation (i.e., they are based only on the generic prediction error minimization and precision updating equations described above); and these types of predictions have been successfully associated with neural responses observed in previous functional magnetic resonance imaging [fMRI] and EEG studies (e.g., see ( Schwartenbeck et al, 2015 ; Smith et al, 2021e ; Whyte, Hohwy, & Smith, 2021 ; Whyte & Smith, 2020 ). However, cortical column proposals – such as that shown in Fig.…”

Section: Neural Process Theorymentioning

confidence: 99%

See 2 more Smart Citations

A step-by-step tutorial on active inference and its application to empirical data

Smith

Friston

Whyte

2022

Journal of Mathematical Psychology

Self Cite

125

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Section: Introductionsupporting

confidence: 79%

Section: Modeling Learningmentioning

confidence: 99%

Section: Neural Process Theorymentioning

confidence: 99%

See 1 more Smart Citation

A step-by-step tutorial on active inference and its application to empirical data

Smith

Friston

Whyte

2022

Journal of Mathematical Psychology

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125

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“…Nevertheless, a unique benefit of our approach is that future studies could combine the HRD with computational modelling and direct neuronal recordings to conclusively establish the potential link between these parameters, and to tease apart the contributions of prior versus sensory precision to the imprecision observed here in heart-rate decisions (see e.g. Allen et al, 2019;Smith et al, 2020Smith et al, , 2021 for potential modelling applications).…”

Section: Discussionmentioning

confidence: 99%

The heart rate discrimination task: a psychophysical method to estimate the accuracy and precision of interoceptive beliefs

Legrand

Nikolova

Correa

et al. 2021

Preprint

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Interoception - the physiological sense of our inner bodies - has risen to the forefront of psychological and psychiatric research. Much of this research utilizes tasks that attempt to measure the ability to accurately detect cardiac signals. Unfortunately, these approaches are confounded by well-known issues limiting their validity and interpretation. At the core of this controversy is the role of subjective beliefs about the heart rate in confounding measures of interoceptive accuracy. Here, we recast these beliefs as an important part of the causal machinery of interoception, and offer a novel psychophysical "heart rate discrimination" method to estimate their accuracy and precision. By applying this task in 218 healthy participants, we demonstrate that cardiac interoceptive beliefs are more biased, less precise, and are associated with poorer metacognitive insight relative to a matched exteroceptive control. Our task, provided as an open-source python package, offers users an intuitive, robust approach to quantifying cardiac beliefs.

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“…Outside of neuroscience, there are also several other lines of research in psychology and cognitive science modelling perception (Kersten, Mamassian, & Yuille, 2004), categorization (Lake, Salakhutdinov, & Tenenbaum, 2015;Sanborn, Griffiths, & Navarro, 2010;Smith, Schwartenbeck, Parr, & Friston, 2020), and several other cognitive processes in terms of Bayesian inference (Tenenbaum, Kemp, Griffiths, & Goodman, 2011). Both empirical and simulation studies suggest that such models can account for a wide range of behavioral and neuroscientific findings (e.g., see (K. Friston, FitzGerald, Rigoli, Schwartenbeck, & Pezzulo, 2017;Smith, Badcock, & Friston, 2021;Smith, Mayeli, et al, 2021;Smith, Schwartenbeck, Stewart, et al, 2020;Walsh, McGovern, Clark, & O'Connell, 2020;Whyte & Smith, 2021;F. Xu & J.…”

Section: Introductionmentioning

confidence: 99%

Why Bayesian brains perform poorly on explicit probabilistic reasoning problems

Smith¹,

Ramstead²,

Kiefer³

2022

Preprint

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There is a growing body of evidence suggesting that the neural processes underlying perception, learning, and decision-making approximate Bayesian inference. Yet, humans perform poorly when asked to solve explicit probabilistic reasoning problems. In response, some have argued that certain brain processes are Bayesian while others are not; others have argued that reasoning errors can be explained by either inaccurate generative models or limitations of approximation algorithms. In this paper, we offer a complementary perspective by considering how a Bayesian brain would implement conscious reasoning processes more generally. These considerations require making two distinctions, each of which highlights a fundamental reason why Bayesian brains should not be expected to perform well at explicit inference. The first distinction is between inferring probability distributions over hidden states and representing probabilities as hidden states. The former assumes that the brain’s dynamics instantiate a form of approximate Bayesian inference, premised on a model of how observations are generated by hidden states of the world. In contrast, the latter assumes the brain represents probabilities themselves as hidden states – namely, hypotheses about the correct answers to explicit reasoning problems. In this latter case, correctly inferring the most likely probability to report would implausibly require the brain to possess a generative model encoding Bayes’ theorem itself. The second distinction is between inference and mental action. In addition to state inference, consciously solving Bayes’ theorem requires the selection of a particular sequence of goal-directed cognitive actions (e.g., mental multiplication and addition, followed by division). While Bayesian brains infer probability distributions over action sequences, the possible sequences themselves often need to be learned. These considerations show that, regardless of the specific generative model in question or approximation algorithm employed, and even if all brain processes were Bayesian, an innate proficiency at solving explicit probabilistic reasoning problems should not be expected.

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Gut inference: A computational modelling approach

Cited by 48 publications

References 114 publications

A step-by-step tutorial on active inference and its application to empirical data

A step-by-step tutorial on active inference and its application to empirical data

The heart rate discrimination task: a psychophysical method to estimate the accuracy and precision of interoceptive beliefs

Why Bayesian brains perform poorly on explicit probabilistic reasoning problems

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